Editor’s Note: Fuzzy cognitive maps can play an important role in decision making. This article shows how a myriad of attributes can be mapped to determine the probability of success of a distance learning course.

A Methodology for Evaluating a Course
for Distance Education

Karl Perusich and Kevin Taylor

USA

Abstract

Determining whether a technical course will succeed if delivered through some distance education means involves judging how a myriad of attributes will interact and impact its success or failure. Although some of these attributes should be common to most courses, instructors and curricula, many are specific to the institution and its educational environment. To assess the efficacy of a course for distance education, fuzzy cognitive maps are proposed as a methodological technique for capturing the unique attributes and interactions that must be examined. To illustrate the technique a representative fuzzy cognitive map is constructed for a generic technical course and used to assess it chances for success through remote delivery under various conditions.

Keywords: fuzzy cognitive maps, distance education, asynchronous lectures

Introduction

Increasingly engineering and engineering technology programs are evaluating the efficacy of non-traditional delivery mechanisms for coursework. This is being driven in part by the need to increase class sizes to meet financial constraints while maximizing the accessibility of students to particular faculty expertise. It is also, in part, the result of enhanced capabilities in computer networking, video teleconferencing, remote access to laboratory equipment and to a variety of other new Internet-interfaceable technologies. [1] But not every course is or should be considered a candidate for distance education. A variety of attributes about the course, the instructional environment in which it takes place and the technology available to support its delivery all interact to determine its success or failure. Drawing on the experiences of the authors, this paper delineates these attributes and their interactions to provide insight into how to assess the likelihood that a particular class will be received by students and faculty positively if delivered through some remote technique. [2]

Any assessment of a course for its potential for distance delivery necessarily must incorporate a variety of attributes. [3] To fully predict whether a course will succeed or fail if delivered through some remote mechanism requires more than a listing of these attributes. It requires an understanding of how these attributes will interact with each other to enhance or mitigate certain effects. Although one can define certain attributes and interactions as “universal” that will probably occur with any distance education course, many of these will be unique and specific to instructors, students, institution and educational environment in which it takes place. As such the intent of this paper is to develop a methodology rather than an answer for evaluating whether a course can be successfully delivered as distance education.

To provide a framework for the evaluation of a course, several assumptions will be made about techniques for distance delivery of a course and about the environment in which this delivery takes place. Although teleconferencing technology has the potential to develop new paradigms for technical education that replace the traditional lecture/laboratory format, these will only be mentioned in passing. [4] For this paper, it is assumed that the candidate distance education course will be structured in such a way as to generally mimic a traditional lecture/laboratory format. An additional assumption will be that, to the extent possible with available technology, lecture and laboratory materials will be delivered to students through some distance communications means, such as the Internet, with the goal being minimal visitation by a faculty member to the remote site.

In our case, the following attributes were used for judging whether a distance education course was a success or a failure. Any distance education offering should not require substantial additional expenditures of financial resources. If it does, then the first rationale for offering distance education is not met. If students will not take the courses and faculty will not teach them, then adding faculty around the state of Indiana will not provide the distributed foundation of faculty expertise required to meet the second rationale listed above, namely, to allow all students access to faculty expertise within Purdue Statewide Technology regardless of location. Thus, to satisfy the above rationale, both students and faculty should be satisfied with the results. This, then, provides three attributes for evaluating a course for potential distance implementation: whether additional financial resources are required, whether faculty are satisfied with the results and whether students are satisfied with the results.

Fuzzy Cognitive Maps

As will be described below, a variety of attributes can be identified that will impact whether a candidate course for distance education will likely succeed. These attributes interact in a variety of ways (some of which are not always obvious) to determine an overall assessment of the likeliness that a candidate course can be effectively delivered using some means of remote access. A tool is needed that can accommodate diverse types of attributes and their interactions in assessing whether a course is a good candidate for distance education. The tool chosen here is a fuzzy cognitive map.

A fuzzy cognitive map is a signed di-graph that captures the cause/effect relationships that subject matter experts believe exist about a problem. [5] Causes and effects are represented in a map as nodes, with signed, directed edges between nodes indicating the existence of a relationship. [6] Nodes are restricted to three numeric values: 1, 0 and -1, with these values indicating an increase, no change and a decrease, respectively, in the underlying concept represented by the node. In some cases, the underlying concept represented by a node is such that it realistically has only two values, 1 and 0, representing the presence or absence of this attribute respectively. Thus, nodes in a fuzzy cognitive map capture changes in the concepts represented by a node.

Fuzziness enters the map through the edge strengths. Each edge is given a value on the interval [-1,1] to indicate the strength of the relationship between the nodes. Negative values indicate inverse causality, i.e. increasing the cause decreases the effect. Positive values indicate direct causality, i.e. increase the cause and the effect increases. Fractional values for the edge strengths indicate partial causality, and are used to capture linguistic modifiers such as somewhat, a little, a lot, etc. [6]

The fuzzy cognitive map is a true model of a problem and its solution in the sense that outputs can be predicted when inputs are applied. In a fuzzy cognitive map, certain nodes are designated as inputs, with the actual set contextual. The actual input nodes are dependent on the available data and what information is desired, with the representative nodes changing as the context of the problem changes. [7] These input nodal values are applied to the map and become sources of causality, much in the same way that voltage sources become sources of energy in an electric network, and are held constant throughout the inference process.

With the input nodes applied, the values are propagated through the map until it either equilibrates to steady values or oscillates between several values. The inferred value for a node is calculated by summing all nodal values causing it weighted by their edge strengths. A thresholding function is then applied to the result to map the result of the summation to one of the three valid state values (-1, 0, +1). The inferred output is the value of all nodes in the map that result from the application of the inputs, with the totality of the nodes representing a “state of affairs” for the system given the input nodal values. [8]

Fuzzy Cognitive Map for Distance Delivery of Courses

The following figure is a graphical representation of the fuzzy cognitive map constructed by the authors to understand how different attributes interact to determine the success or failure of a remotely delivered course. Each node within the map represents some variable quantity that has, can or will impact a distance education course. The attributes used were obtained from the literature, from personal experience with delivering remote coursework and through interviews with students that have taken them. [9,10] As constructed the map has a purely feed-forward structure; feedback is not present. Causality in the map flows from basic characteristics of the course and delivery environment like Quality of Text and Similarity of Equipment Onsite-Host, to the value judgment nodes of Resources Required, Faculty Satisfaction and Student Satisfaction.[1]

Although there are 24 different attributes represented in the map they naturally cluster into six areas: lecture resources, laboratory resources, delivery method, student satisfaction, faculty satisfaction and resources required, with some overlap of nodes.

Lecture Resources

Since it is assumed here that any distance education course will attempt to mimic a traditional lecture/laboratory format and not try to use any new paradigm for content delivery, the quality of traditional classroom resources at the remote site are very important to the overall success of the course. Quality of Text refers to the usefulness of a text for the class as resource for students in understanding the material. The better the text, the clearer its explanations and the more closely it matches the content of the course, the more likely a student at a remote site is to find it useful in helping him/her grasp the material being presented. Thus, Quality of Text is a cause of Usefulness as a Resource in the map with direct causality (+ edge strength).

Quality of Lecture refers to the organization of the class and the clarity of the explanation provided in the classroom. Poor explanations provided in the classroom reduce their value as a resource for the student in mastering the material (Usefulness as a Resource) and necessitating the need for personal interaction with the instructor for additional clarification of a concept (Access to Instructor for Consultation). [11] In a distance education course this interaction is usually accomplished by phone, through electronic means such as email or online chat, or with a periodic visit by the instructor to the remote site. Regardless of the method, the more opportunities provided the students to get clarification on a topic or questions answered by an instructor (Access to Instructor for Consultation), the more useful such interaction is going to be to the student (Usefulness as a Resource).

Figure 1. Fuzzy Cognitive Map of Remote Delivery of Technical Courses

The more the need for the instructor to be onsite (Access to Instructor for Consultation) though, the more time the faculty member will spend on the course (Time Faculty Spends on Course) and the greater the need for the instructor to actually visit the remote site (Need for Faculty Onsite).

The more useful these secondary resources are to the student (Usefulness as a Resource), the more likely they are to understand the material (Help Students Understand Material). This understanding is also predicated on a timely response to questions. To be of value in grasping material, a question must be answered quickly enough that the student understands it before they are tested on it, or before the concept is used as the basis for the development of a succeeding concept (Response Times to Questions). Anything that tends to decrease the response time will tend to increase the likelihood that the student will understand the material.[2]

Students that understand the material presented in class (Help Students Understand Material) will more than likely also get good grades (Good Grades).

Laboratory Resources

Of the two components to a traditional technical course, probably the more difficult to reproduce in a distance education class is the laboratory experience. The greater the similarity between the equipment at the host and remote sites (Similarity of Equipment Onsite-Host), the less is the need for assistance by the instructor or a laboratory assistant in teaching the students how to use particular equipment or take certain measurements (Need for Help on Using Equipment). Dissimilar equipment will require different settings and, in somewhat extreme cases, may actually look different from equipment at the host location. In this case, the laboratory procedures must be re-written (Need to Rewrite Labs) or the instructor (or an assistant) provided onsite to clarify the procedures (Need for Faculty Onsite). Likewise, if the students have previous experience using the equipment (Student Experience with Equipment), the less they will need onsite assistance in doing a particular exercise or laboratory procedure.

Well written, well documented laboratory procedures are also a necessity. In an onsite course, the instructor has less need to be absolutely thorough in their laboratory instructions because they will be present when the students work on the problem and can immediately answer questions or troubleshoot problems. They can, in fact, “forget” to document things and the exercise can still be a success. Poorly or incomplete laboratory procedures at a remote site, though, will hinder the progress of the students in their task thereby requiring additional time to be devoted to the laboratory by both the instructor, who may have to visit the remote site to recast the procedures to make them compatible with the local equipment, and the student, who may have to defer completion of the exercise until further information is provided. Well written, well documented laboratory procedures (Well Documented Laboratory Procedures) are required to reduce the need for the faculty to visit the remote site (Need for Faculty Onsite) and prevent any delays in students completing them.

Delivery Method

The particular methods used to deliver the distance education course, whether it is the lecture or the laboratory portion, can have a significant impact on its viability. Since the authors only experience has been with asynchronous delivery of lecture (taped and made available through the network), the analysis will concentrate on this type of remote access for lecture. Using asynchronous delivery (Asynchronous Lectures) of lectures requires potentially extensive translation and preparation of the recorded material to make it available on the Internet (Translation and Preparation Time).[12]The time that it takes to prepare the material for student use can negatively impact it usefulness as a resource for them. Any increase in the preparation time (Translation and Preparation Time) will decrease the likeliness of its timely delivery to the students (Timely Delivery). If materials are late in getting to students they may miss or need to delay assignments and laboratory exercises thus reducing their usefulness to the learning process (Usefulness as a Resource). However, preparing recorded materials for delivery to students in a short period of time will require a potentially significant effort by a technician or other support staff member (Technician Time).

Student Satisfaction

Student satisfaction is one of three key attributes a distance education course (and distance education experience) must have. Remote delivery of courses has been proposed as a mechanism for solving or alleviating problems schools face with in the new reality of limited budgets and declining enrollments. But remote delivery of courses will have no effect on these problems if students avoid them. And students who have had a poor experience with a remotely delivered course will not enroll in future offerings.

The exact factors determining whether a student will be satisfied with a course will vary greatly by context, by subject and by their personal psychology. As such, any criteria used to judge “student satisfaction” are necessarily generalizations. The authors assumed that a student will be satisfied with the outcomes of a course if they learned the material, thereby giving them the foundation to apply the concepts to real-world problems, and if their grades were “good”. Good grades would be an indicator of mastery of the material and an acceptable reward for the efforts expended by a student in taking the course. In the fuzzy cognitive map, Student Satisfaction has two causes, Help Students Understand Material and Good Grades. Any resource or effort in delivering the distance education class that increase either of these will improve the satisfaction of the student with the course.

Faculty Satisfaction

Like students, faculty must be satisfied with the distance education experience or they will avoid teaching other courses in this manner. Faculty satisfaction (or dissatisfaction) can be affected by both the additional effort they must expend in teaching the course and by the results, as determined by the level of student performance in the class. If a distance education class ends up requiring or needing a significant level of effort beyond a regular course taught onsite (Time Faculty Spend on Course), it will create faculty dissatisfaction with remote delivery of classes (Faculty Satisfaction). Faculty members are much less likely to voluntarily participate in future distance education classes under these circumstances.

When dealing with two different locations, there is the possibility that they will have different academic schedules. Dissimilarity in academic schedules can have very negative effects on faculty satisfaction. If holidays, vacations, final exam schedules, starting dates and ending dates for the semester are different, instructors may find themselves reteaching or repeating materials several times thereby increasing their work load. Thus, the lack of identical schedules (Identical Schedules) will tend to increase the work load for the faculty (Time Faculty Spend on Course) and decrease their satisfaction with the distance education experience (Faculty Satisfaction).

Any need for faculty to be onsite at the remote location will negatively directly and indirectly affect the satisfaction of the instructor. The need to be onsite (Need for Faculty Onsite) directly reduces faculty satisfaction (Faculty Satisfaction) because it requires travel time to reach the remote location that could have been better spent doing other things. Indirectly it will reduce faculty satisfaction because it invariably increases the work (and time) required for the course (Time Faculty Spends on Course).

Resources Required

The final cluster of nodes in the map involves the resources required for delivery of the distance education course. One of the determining factors for many institutions in the development of distance education classes is the need to increase the access of students to courses while minimizing additional expenditures to do so (Resources Needed). In this framework, the more successful distance education courses are going to be those that are implemented with minimal additional resources. Classes that require significant purchases of new equipment (Need to Buy New Equipment), large expenditures for travel (Travel Costs), more support staff (Technician Time), or considerable release time for faculty with the need possibly to reassign duties to others (Time Faculty Spends on Course) are not likely to be viewed favorably by the administration. In the extreme case, if the total of new resources needed exceeds some threshold, it may make more sense financially to hire an additional faculty member at the remote site.

These clusters of nodes capture the basic relationships that exist for evaluating a course as a candidate for remote delivery. In most cases the input nodes, given in table 1, are assessable for a course prior to its implementation. With this assessment in hand, these nodal values can be applied to the map to infer whether the constituencies involved with the course will be satisfied with the results. Possible sources of information for assessing values for the different input nodes include class surveys, instructor evaluations, laboratory inventories, faculty surveys or expert judgments.

Table 1

Inputs Nodes for Fuzzy Cognitive Maps
of Remote Delivery of Technical Courses

Candidate Courses

By assessing values for some or all of the input nodes given in table 1, an inference can be made about the expected levels of satisfaction of students and faculty participating in a course, and the resources required to implement it. Ideally, all of the constituencies involved in the course will be either satisfied or not satisfied, making the decision about offering the course easier.[3]

In all likelihood, though, it is possible for the inferred judgment nodes to be contradictory in the sense that some indicate satisfaction while others indicate dissatisfaction. It has been the authors’ experience in developing and delivering distance education coursework that all three of the principal players involved in the distance education course, the students, the faculty and the administration, must be satisfied with the result or they will not participate in them again. As such, the map should infer for any candidate course that the students and faculty are satisfied (Student Satisfaction is +1 and Faculty Satisfaction is +1) and that the additional resources needed to offer the course are minimal (Resources Required is 0 or -1). Courses that give other combinations that do not meet this stringent requirement will be considered inappropriate candidates for delivery through distance education.

When evaluating a course not all nine defined input nodes given in table 1 need to have values for the map in order to make an inference. The fact that the map will infer results without a complete set of input nodes is an important feature of this technique. However, the more nodes that are assigned values, the better the “quality” of the inference. If only one nodal value was available, for example Quality of Text, then a decision maker might be justifiably suspicious of the inference from the map because so little data was used. On the other hand, if seven of the nine input nodes provided values, then, although still not complete, the inference may be deemed acceptable. Ultimately it will be up to the decision maker to judge whether to accept inferences from the map made with fewer than all of the input nodes.

Candidate Courses

Four example candidate courses will be evaluated using the fuzzy cognitive map just provided: good lecture and laboratory attributes, bad attributes for both, good lecture but bad laboratory resources and bad lecture but good laboratory resources. For these the input nodes are clustered into lecture and laboratory resources, and are presented in table 2. In all cases asynchronous lectures are assumed. These four examples were chosen because two (good attributes for both and bad attributes for each) tend to validate the inferences made by the map, and because two (good attribute for one and bad attribute for the other) illustrate additional possibilities for real world situations. These four examples also specifically cover a broad range of the types of situations instructors and administrators might find themselves in when evaluating candidate courses for distance education. Individual nodes within each cluster can be changed to reflect the actual situation present.

Table 2

Input Node Classification

Nominal Case: Good Lecture and Good Laboratory Attributes

This case represents the best of all possible worlds. The attributes of the lecture are such that they fit well with the characteristics expected for a course that will succeed through distance delivery, the textbook is good as are the lectures delivered by the instructor. Additionally, laboratory procedures are well written, the students have previous experience with the equipment and software, and the equipment at the remote site is the same or significantly similar to that at the host site. The resulting map is given in figure 2.

In the map, these nodes are given values of +1 (dark gray) to indicate an increase in or presence of the underlying concept represented by the node in the map. Only one input node, Response Time to Questions, is given a -1 value. This is to indicate that there is a decrease in the response time to questions which is actually positive for the overall situation. The inputs and outputs for the map are given in the following table.

Table 3

Input/Output Nodes for Fuzzy Cognitive Map

Light gray indicates a nodal value of -1, dark gray a nodal value of +1;
no background color a nodal value of 0.

Figure 2. Nominal Case: Good Lecture, Good Laboratory Setup

Following the procedures outlined above, these input values are propagated through the value to infer state values for the three output nodes: Resources Required, Faculty Satisfaction and Student Satisfaction. The inferred values in this case match the requirements given previously for a successful distance education course. Resources Required decreases (light gray, -1), Faculty Satisfaction increases (dark gray, +1) and Student Satisfaction also increases (dark gray, +1).

Nominal Case: Bad Lecture and Bad Laboratory Attributes

This case is the dual to the previous example and represents the worst of all possible worlds. For this example none of the lecture attributes nor any of the laboratory attributes meets the necessary standards for a successful distance education experience for the students, faculty or university administration. The text is poor as are the lectures. Response time to student questions is lengthy. Class schedules between the host and remote site are not in sync. The laboratory equipment is different between the host and remote sites, and students have little useful experience with it. Laboratory procedures are poorly written.

Light gray indicates a nodal value of -1, dark gray a nodal value of +1;
no background color a nodal value of 0.

Figure 3. Nominal Case: Bad Lecture, Bad Laboratory Setup

All of the input nodes is this case are assigned values of -1 (dark gray) to indicate a decrease or absence of the concept, except Response Times to Questions. This nodes is assigned a value of +1 (light gray) to indicate an increase in the time it takes students to get their questions answered. The map is allowed to equilibrate inferring that the resources required increase and both student and faculty satisfaction decrease. This state of affairs would suggest that the candidate course being examined is not suitable for remote delivery.

Case: Good Lecture and Bad Laboratory Attributes

The two nominal cases just presented represent the two possible extremes for the characteristics of a candidate course. Unfortunately, “real” courses will have a mix of attributes that make it fall somewhere in between the extremes of the best of all possible worlds and the worst of all possible worlds. Two additional cases will be examined that fall into this category. For this case, the lecture attributes of the course are assumed to be good, but the attributes of the laboratory portion of the course fall short. Class schedules are identical. The text and lectures are good, but the laboratory procedures are poorly documented. Additionally, the equipment at the remote site is significantly different from the equipment at the host site and the students taking the remote course have little experience with it. The map with these inputs is given in figure 4.

Given these inputs, good lecture and poor laboratory characteristics, the map infers an increase in the resources required to operate the course (Resources Required, +1), ambivalence about faculty satisfaction (Faculty Satisfaction, 0), and an increase in student satisfaction, (Student Satisfaction, +1). Given the strict requirements listed previously, a course with these attributes would not be a good candidate for distance delivery. An increase in resources would be required to offer it, and faculty satisfaction does not increase.

This case also illustrates another use for the map, identifying interventions, i.e. ways to change, that might alter the results in the desired direction. Because the map is constructed as a detailed series of cause/effect relationships, it can also be used to identify which, if any, relationships must be altered or broken to affect a change. For this case, assume that an increase in resources is not as important as increasing faculty satisfaction. A candidate course must provide both faculty and student satisfaction, with an increase in resources allowable. Back chaining through nodes in the map from output to inputs can be used to identify possible ways to change them or possible ways to alter the structure of the map to change the outcome. Altering the structure, though, would require a change in the environment modeled by the map.

Examining figure 4, faculty satisfaction is caused by three nodes, one of which is already positive for the desired outcome, Good Grades. Time Faculty Spend on Course would be a good candidate because two of its four causes are already positive for the desired outcome, Access to Instructor for Consultation and Identical Schedules. Of the two remaining causes the simplest to alter would be Need to Rewrite Labs because it has a single cause. In this case it might be possible to change its cause, Similarity of Equipment Onsite-Host, by buying the same equipment for both sites. This would change the value of this input node from -1 (dissimilar) to +1 (similar). Propagating this value through the map will in fact change the output Faculty Satisfaction to +1, the desired outcome, while leaving the other two output nodes with their same values.

Light gray indicates a nodal value of -1, dark gray a nodal value of +1;
no background color a nodal value of 0.

Figure 4. Good Lecture, Bad Laboratory Setup
 

Case: Bad Lecture and Good Laboratory Attributes

The final case examined is the opposite of the one just presented. In this case, given in figure 5, the laboratory attributes of the course are good, but the lecture attributes are poor. The equipment is similar or the same between the host and remote sites, students have experience with it and the laboratory procedures are well documented. On the other hand, the quality of the text and lectures is poor. The map infers that resources required will decrease, faculty satisfaction will increase, but student satisfaction will decrease. A course with these attributes does not meet the required criteria because student satisfaction has decreased.

Again, back chaining can be used in the map to identify environmental changes or interventions that might be used to alter the state of affairs to the one desired, in this case, an increase in student satisfaction. Examining the map in figure 5, there are really only two inputs that can be altered that will change student satisfaction: Quality of Lecture and Quality of Text. Both must change to increase (+1) for Usefulness as a Resource to increase (+1). Propagating this value through the map will in fact change student satisfaction to increase (+1). Thus, in this case both the quality of the lectures and the text must be changed to alter the inference in the desired way.

Light gray indicates a nodal value of -1, dark gray a nodal value of +1;
no background color a nodal value of 0.

Figure 5. Bad Lecture, Good Laboratory Setup
 

Conclusion

When evaluating candidate courses for distance education, it must be recognized that its success or failure will depend on the complex interaction of variety of different attributes. These attributes do not always interact in an obvious or intuitive way. Further, many of these attributes and interactions are specific to the course, students, instructor and institution offering it. To model these interactions for evaluating a course, fuzzy cognitive maps were chosen because they can seamlessly capture these different interactions. To illustrate the process, a map was constructed that incorporated a variety of attributes, lumped loosely around lecture, laboratory and resource characteristics that might impact whether a technical course would succeed or fail through remote delivery. Success or failure was assessed by resources required, faculty satisfaction and student satisfaction. In the variety of cases examined no clear criteria emerged as generalizable about whether a course would be a good or bad candidate for remote delivery. Thus, careful evaluation must be done when assessing a course for remote delivery from a host campus.

References

[1]  T. Shih, “Distance Education Technologies: Current Trends and Software Systems”, Proceedings of the First International Symposium on Cyber Worlds, Nov. 6-8, 2002,
pp. 38-43.

[2]  K.D. Taylor, J.P. Denton and K.A. Perusich, “A Distance Education Course with both Lecture and Laboratory Components”, Proceedings of the iCEER 2004 International Conference on Engineering Education. June 27-30, 2004 Session T06b.

[3]  A. Ibrahim, “Assessment of Distance Education Quality Using Fuzzy Sets Model”, Proceedings of the International Conference on Engineering Education, Aug. 6-10, 2001, pp. 6E4.8-6E4.10.

[4]  S. Bray and K. Scaizo, “Evolution vs. Revolution: Integrating and Migrating Technologies for Distance Learning,” Proceedings of the 2002 Conference for Industry and Education Collaboration, 2002, Session 434.

[5]  B. Kosko, “Fuzzy Cognitive Maps”, International Journal of Man-Machine Studies, vol. 24, 1986, pp. 65-75.

[6]  B. Kosko, “Adaptive Inference In Fuzzy Knowledge Networks.” IEEE International Conference on Neural Networks, June 1987, pp. II-261-268.

[7]  K. Perusich, and M. D. McNeese, "Using Fuzzy Cognitive Maps to Define the Search Space in Problem Solving," In Design of Computing Systems: Cognitive Considerations, G. Salvendy, M. Smith, and R. Koubek (Eds.), Amsterdam, The Netherlands: Elsevier Science Pub., pp.805-808, 1997.

[8]  K. Perusich, and M. McNeese, “Using Fuzzy Cognitive Maps for Data Abstraction and Synthesis in Decision Making”, Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society, 1997, pp. 5-9.

[9]  C. DeVilliers, M. Pistorius, P. Alexander and N. duPlooy, “Constraints on Computer-Assisted Instruction in a Distance education Environment,” Computing and Control Engineering Journal, vol. 3, no. 1, 1992. pp.11-13.

[10] M. Shepherd and D. Amoroso, “Designing a Distance Education Program: the University of Colorado at Colorado Springs”, Proceedings of the 31^st Hawaii International Conference on System Sciences, Jan. 6-9, 1998, pp. 547-558.

[11] J. Dutton, M. Dutton and J. Perry, “Do Online Students Perform as Well as Lecture Students?”, Journal of Engineering Education, Jan. 2001, pp.131-136.

[12] R. Bittencourt and D. Carr, “A Method for Asynchronous, Web-Based Lecture Delivery”, FIE 2001 Reno: 31^st Annual Frontiers in Education Conference, vol. 2, 2001.
pp. F2F.12-F2F.17.
 

About the Author

Karl Perusich, Ph.D. is an associate professor of electrical and computer engineering technology at Purdue University-South Bend. He has been actively involved in the development of distance education techniques for the delivery of technical courses. His research interests include qualitative modeling techniques including fuzzy methods.

Karl Perusich
Purdue University
1733 Northside Blvd.
South Bend, IN 46634-7111

(574)520-5508

Email: perusich@purdue.edu

Kevin Taylor is an Associate Professor of Electrical & Computer Engineering Technology at Purdue University in the College of Technology at Kokomo. He is the co-Affiliate Director of Project Lead the Way Indiana, a program aimed at raising the awareness of engineering and technology in high schools. Kevin serves as an At-Large Member of the Executive Committee of the Technology Accreditation Commission (TAC) of ABET and is a registered Professional Engineer.

Kevin Taylor
Purdue University
2300 S. Washington St.
PO Box 9003
Kokomo, IN 46904-9003

(765)455-9228

Email:  kdtaylor@purdue.edu